System and method for content delivery optimization based on a combined captured facial landmarks and external datasets

ABSTRACT

A system and method to turn computer vision captured data of a subject into an optimized rearranged media content to a display device using historic or real-time data points captured by facial analytics software comprising a media compiler computer that receives a digital media segment and metadata describing the digital media segment, the metadata comprising, at least, a priority marker for each frame of a plurality of frames and one or more set durations and create a new digital media segment by rearranging at least a portion of the plurality of frames in combination with additional elements such as generated text, zooming in on focal items within the media and other techniques for highlighting key elements of the new digital media segment, the rearrangement based on priority markers associated to each frame and other pre-configurations.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of, and priority to United StatesProvisional Patent Application Ser. No. 62/639,128, filed Mar. 6, 2018,entitled “SYSTEM AND METHOD FOR CONTENT DELIVERY OPTIMIZATION BASED ON ACOMBINED CAPTURED FACIAL LANDMARKS AND EXTERNAL DATASETS”, the entirespecification of which is incorporated herein by reference in itsentirety.

BACKGROUND Field of the Art

The disclosure relates to the field of delivering digital media based onattention conditions and influencing data sets, and more particularly tothe field delivering automatic compiled digital media based on real-timeattention conditions and influencing data sets.

Discussion of the State of the Art

In the field of artificial intelligence (AI) content generators haveemerged as the offspring of word spinners. These were often freeservices where users could place text and the website would findsynonyms for most words used in the sample. A problem with known systemsin the art, is that these words would be replaced without taking thecontext into account and, more often than not, result in a piece of textwhich makes little to no sense at all. Word spinners have evolved intoAI Content generators that take existing content and rewrite and shuffleit around to create new content. Content can come from a variety ofsources including answers from questions on social media and forums.

Automated content generation is becoming prominent in other forms ofmedia, such as video. For example, providers of video automation toolsare commonly used for creating text-based videos; however, theseservices are not without their limitations; relying on the AI to converttext-to-video can create some sub-standard results, so there is still aneed for human input, particularly for organizations with a higherreputation.

Brands such as The Wall Street Journal™, TopFan™ and Pandora™ are usingAI strategies to boost conversion rates and identify audiencepreferences. Specifically, Pandora uses AI-fueled machine learningalgorithms to program new songs that listeners will enjoy. This strategycombines human input with AI technology to filter out recordings thatare either duplicates or of lesser quality.

There are factors which inherently prevent AI content generators fromcompletely phasing out human writers completely. To begin with, contentproduced by artificial intelligence often lacks the flair added by humanwriters to make articles more engaging to read; writers have anecdotesthey can include to create a more emotional response. Other problemsinclude the cost of producing AI content and robots lack the influencewell known writers would have.

What is needed is a system and method to understand attention conditionsof subjects viewing media by capturing and analyzing video of thesubjects, and to dynamically resequence, in real-time, new mediasegments based on attention information associated to identified viewersin combination with additional elements to highlight priority elementswithin media such that viewers will see information in a prioritysequence based on parameters set within the video. Said differently,what is needed are systems and methods for content creators to ensurethat the most relevant and important portions of media, with additionalelements to highlight content, are consumed first and based on specificbehaviors of viewers.

SUMMARY OF THE INVENTION

The inventor has conceived and reduced to practice, in a preferredembodiment of the invention, a system and method to turn computer visioncaptured data of a human subject into delivered content that is receivedby that person in an optimized format. According to a preferredembodiment of the invention, a digital media arrangement system based onan attention condition and influencing data sets, is disclosed,comprising a media compiler computer comprising a memory, an imagecapture component, a display component, one or more processors, and aplurality of programming instructions, the plurality of programminginstructions stored in the memory and when executed by the one or moreprocessor, cause the one or more processor to receive a digital mediasegment and metadata describing the digital media segment, the metadatacomprising, at least, a priority marker for each frame of a plurality offrames and one or more set durations, each set duration being an amountof time associated to a set of frames from an administrator device. Themedia compiler computer may be further operable to capture images ofsubject for facial recognition and facial analytics, or users andidentifying a user profile associated to the user, the user profilecomprising, at least, one or more attention conditions; The frame mediacompiler computer may be further operable determining a sequence offrames based on a first attention condition of the one or more attentionconditions and influencing data sets to compile a new digital mediasegment by rearranging at least a portion of the plurality of frames,the rearrangement based on priority markers associated to each frame,and then displaying the new media segment to a display component whereinthe set of associated to a same priority marker.

In a preferred embodiment, an attention condition comprises a durationof time based on a timed calculation of a user's head position being inan in-angle arrangement.

In some embodiments, the attention condition may be an average durationof time based on a calculation of a plurality of durations of a user'shead position in an in-angle arrangement or based on historicalinformation from a user profile.

In a preferred embodiment, the new digital media segment comprises asequence of frames wherein the total duration of time of the sequence offrames is equal to or less than a duration associated to the firstattention condition.

In some embodiments, no rearrangement of the video segment is performed.

In some embodiments, an influencing data set may comprise behavior,computed mood of an individual, number of people present, a time-of-day,a location, a demographic information associated to the user, and otheritems described herein;

In some embodiments, additional elements may be used to optimize andemphasize attention-based and importance-based information.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

The accompanying drawings illustrate several embodiments of theinvention and, together with the description, serve to explain theprinciples of the invention according to the embodiments. It will beappreciated by one skilled in the art that the particular embodimentsillustrated in the drawings are merely exemplary and are not to beconsidered as limiting of the scope of the invention or the claimsherein in any way.

FIG. 1 is a block diagram illustrating an exemplary hardwarearchitecture of a computing device used in an embodiment of theinvention;

FIG. 2 is a block diagram illustrating an exemplary logical architecturefor a client device, according to an embodiment of the invention;

FIG. 3 is a block diagram showing an exemplary architectural arrangementof clients, servers, and external services, according to an embodimentof the invention;

FIG. 4 is another block diagram illustrating an exemplary hardwarearchitecture of a computing device used in various embodiments of theinvention;

FIG. 5A is a block diagram illustrating an exemplary systemarchitectural arrangement of system components, according to a preferredembodiment of the invention;

FIG. 5B is a block diagram illustrating an exemplary architecturalarrangement of system components, according to a preferred embodiment ofthe invention;

FIG. 5C is a block diagram illustrating an interaction of a plurality ofalgorithms as they relate to the data capture and the content delivery,according to a preferred embodiment of the invention;

FIG. 6A is a flow diagram illustrating a calculation of an attentioncondition, according to a preferred embodiment of the invention;

FIG. 6B is an illustration defining a head gaze angle, according to apreferred embodiment of the invention;

FIG. 7 is a block diagram illustrating a structure of a contentmanagement solution (CMS) logic, according to a preferred embodiment ofthe invention.

FIG. 8 is flow diagram outlining trigger functionality, according to apreferred embodiment of the invention;

FIG. 9A is a flow diagram illustrating a smart content deliverymechanism, based on an attention condition, according to a preferredembodiment of the invention;

FIG. 9B is an illustration showing the optimization of video content,according to a preferred embodiment of the invention;

FIG. 10A is an illustration showing an exemplary arrangement ofadditional elements according to a preferred embodiment of theinvention;

FIG. 10B is an illustration showing an exemplary arrangement ofoptimized additional elements according to a preferred embodiment of theinvention.

DETAILED DESCRIPTION

The inventor has conceived, and reduced to practice, a system and methodto turn computer vision captured data of a human subject into deliveredcontent that is received by that person in an optimized format, in theform of a combination of algorithms.

One or more different inventions may be described in the presentapplication. Further, for one or more of the inventions describedherein, numerous alternative embodiments may be described; it should beappreciated that these are presented for illustrative purposes only andare not limiting of the inventions contained herein or the claimspresented herein in any way. One or more of the inventions may be widelyapplicable to numerous embodiments, as may be readily apparent from thedisclosure. In general, embodiments are described in sufficient detailto enable those skilled in the art to practice one or more of theinventions, and it should be appreciated that other embodiments may beutilized and that structural, logical, software, electrical and otherchanges may be made without departing from the scope of the particularinventions. Accordingly, one skilled in the art will recognize that oneor more of the inventions may be practiced with various modificationsand alterations. Particular features of one or more of the inventionsdescribed herein may be described with reference to one or moreparticular embodiments or figures that form a part of the presentdisclosure, and in which are shown, by way of illustration, specificembodiments of one or more of the inventions. It should be appreciated,however, that such features are not limited to usage in the one or moreparticular embodiments or figures with reference to which they aredescribed. The present disclosure is neither a literal description ofall embodiments of one or more of the inventions nor a listing offeatures of one or more of the inventions that must be present in allembodiments.

Headings of sections provided in this patent application and the titleof this patent application are for convenience only and are not to betaken as limiting the disclosure in any way.

Devices that are in communication with each other need not be incontinuous communication with each other, unless expressly specifiedotherwise. In addition, devices that are in communication with eachother may communicate directly or indirectly through one or morecommunication means or intermediaries, logical or physical.

A description of an embodiment with several components in communicationwith each other does not imply that all such components are required. Tothe contrary, a variety of optional components may be described toillustrate a wide variety of possible embodiments of one or more of theinventions and in order to more fully illustrate one or more aspects ofthe inventions. Similarly, although process steps, method steps,algorithms or the like may be described in a sequential order, suchprocesses, methods and algorithms may generally be configured to work inalternate orders, unless specifically stated to the contrary. In otherwords, any sequence or order of steps that may be described in thispatent application does not, in and of itself, indicate a requirementthat the steps be performed in that order. The steps of describedprocesses may be performed in any order practical. Further, some stepsmay be performed simultaneously despite being described or implied asoccurring non-simultaneously (e.g., because one step is described afterthe other step). Moreover, the illustration of a process by itsdepiction in a drawing does not imply that the illustrated process isexclusive of other variations and modifications thereto, does not implythat the illustrated process or any of its steps are necessary to one ormore of the invention(s), and does not imply that the illustratedprocess is preferred. Also, steps are generally described once perembodiment, but this does not mean they must occur once, or that theymay only occur once each time a process, method, or algorithm is carriedout or executed. Some steps may be omitted in some embodiments or someoccurrences, or some steps may be executed more than once in a givenembodiment or occurrence.

When a single device or article is described herein, it will be readilyapparent that more than one device or article may be used in place of asingle device or article. Similarly, where more than one device orarticle is described herein, it will be readily apparent that a singledevice or article may be used in place of the more than one device orarticle.

The functionality or the features of a device may be alternativelyembodied by one or more other devices that are not explicitly describedas having such functionality or features. Thus, other embodiments of oneor more of the inventions need not include the device itself.

Techniques and mechanisms described or referenced herein will sometimesbe described in singular form for clarity. However, it should beappreciated that particular embodiments may include multiple iterationsof a technique or multiple instantiations of a mechanism unless notedotherwise. Process descriptions or blocks in figures should beunderstood as representing modules, segments, or portions of code whichinclude one or more executable instructions for implementing specificlogical functions or steps in the process. Alternate implementations areincluded within the scope of embodiments of the present invention inwhich, for example, functions may be executed out of order from thatshown or discussed, including substantially concurrently or in reverseorder, depending on the functionality involved, as would be understoodby those having ordinary skill in the art.

Hardware Architecture

Generally, the techniques disclosed herein may be implemented onhardware or a combination of software and hardware. For example, theymay be implemented in an operating system kernel, in a separate userprocess, in a library package bound into network applications, on aspecially constructed machine, on an application-specific integratedcircuit (ASIC), or on a network interface card.

Software/hardware hybrid implementations of at least some of theembodiments disclosed herein may be implemented on a programmablenetwork-resident machine (which should be understood to includeintermittently connected network-aware machines) selectively activatedor reconfigured by a computer program stored in memory. Such networkdevices may have multiple network interfaces that may be configured ordesigned to utilize different types of network communication protocols.A general architecture for some of these machines may be describedherein in order to illustrate one or more exemplary means by which agiven unit of functionality may be implemented. According to specificembodiments, at least some of the features or functionalities of thevarious embodiments disclosed herein may be implemented on one or moregeneral-purpose computers associated with one or more networks, such asfor example an end-user computer system, a client computer, a networkserver or other server system, a mobile computing device (e.g., tabletcomputing device, mobile phone, smartphone, laptop, or other appropriatecomputing device), a consumer electronic device, a music player, or anyother suitable electronic device, router, switch, or other suitabledevice, or any combination thereof. In at least some embodiments, atleast some of the features or functionalities of the various embodimentsdisclosed herein may be implemented in one or more virtualized computingenvironments (e.g., network computing clouds, virtual machines hosted onone or more physical computing machines, or other appropriate virtualenvironments).

Referring now to FIG. 1, there is shown a block diagram depicting anexemplary computing device 100 suitable for implementing at least aportion of the features or functionalities disclosed herein. Computingdevice 100 may be, for example, any one of the computing machines listedin the previous paragraph, or indeed any other electronic device capableof executing software- or hardware-based instructions according to oneor more programs stored in memory. Computing device 100 may be adaptedto communicate with a plurality of other computing devices, such asclients or servers, over communications networks such as a wide areanetwork a metropolitan area network, a local area network, a wirelessnetwork, the Internet, or any other network, using known protocols forsuch communication, whether wireless or wired.

In one embodiment, computing device 100 includes one or more centralprocessing units (CPU) 102, one or more interfaces 110, and one or morebusses 106 (such as a peripheral component interconnect (PCI) bus). Whenacting under the control of appropriate software or firmware, CPU 102may be responsible for implementing specific functions associated withthe functions of a specifically configured computing device or machine.For example, in at least one embodiment, a computing device 100 may beconfigured or designed to function as a server system utilizing CPU 102,local memory 101 and/or remote memory 120, and interface(s) 110. In atleast one embodiment, CPU 102 may be caused to perform one or more ofthe different types of functions and/or operations under the control ofsoftware modules or components, which for example, may include anoperating system and any appropriate applications software, drivers, andthe like.

CPU 102 may include one or more processors 103 such as, for example, aprocessor from one of the Intel, ARM, Qualcomm, and AMD families ofmicroprocessors. In some embodiments, processors 103 may includespecially designed hardware such as application-specific integratedcircuits (ASICs), electrically erasable programmable read-only memories(EEPROMs), field-programmable gate arrays (FPGAs), and so forth, forcontrolling operations of computing device 100. In a specificembodiment, a local memory 101 (such as non-volatile random-accessmemory (RAM) and/or read-only memory (ROM), including for example one ormore levels of cached memory) may also form part of CPU 102. However,there are many different ways in which memory may be coupled to system100. Memory 101 may be used for a variety of purposes such as, forexample, caching and/or storing data, programming instructions, and thelike. It should be further appreciated that CPU 102 may be one of avariety of system-on-a-chip (SOC) type hardware that may includeadditional hardware such as memory or graphics processing chips, such asa Qualcomm SNAPDRAGON™ or Samsung EXYNOS™ CPU as are becomingincreasingly common in the art, such as for use in mobile devices orintegrated devices.

As used herein, the term “processor” is not limited merely to thoseintegrated circuits referred to in the art as a processor, a mobileprocessor, or a microprocessor, but broadly refers to a microcontroller,a microcomputer, a programmable logic controller, anapplication-specific integrated circuit, and any other programmablecircuit.

In one embodiment, interfaces 110 are provided as network interfacecards (NICs). Generally, NICs control the sending and receiving of datapackets over a computer network; other types of interfaces 110 may forexample support other peripherals used with computing device 100. Amongthe interfaces that may be provided are Ethernet interfaces, frame relayinterfaces, cable interfaces, DSL interfaces, token ring interfaces,graphics interfaces, and the like. In addition, various types ofinterfaces may be provided such as, for example, universal serial bus(USB), Serial, Ethernet, FIREWIRE™, THUNDERBOLT™, PCI, parallel, radiofrequency (RF), BLUETOOTH™, near-field communications (e.g., usingnear-field magnetics), 802.11 (WiFi), frame relay, TCP/IP, ISDN, fastEthernet interfaces, Gigabit Ethernet interfaces, Serial ATA (SATA) orexternal SATA (ESATA) interfaces, high-definition multimedia interface(HDMI), digital visual interface (DVI), analog or digital audiointerfaces, asynchronous transfer mode (ATM) interfaces, high-speedserial interface (HSSI) interfaces, Point of Sale (POS) interfaces,fiber data distributed interfaces (FDDIs), and the like. Generally, suchinterfaces 110 may include physical ports appropriate for communicationwith appropriate media. In some cases, they may also include anindependent processor (such as a dedicated audio or video processor, asis common in the art for high-fidelity A/V hardware interfaces) and, insome instances, volatile and/or non-volatile memory (e.g., RAM).

Although the system shown in FIG. 1 illustrates one specificarchitecture for a computing device 100 for implementing one or more ofthe inventions described herein, it is by no means the only devicearchitecture on which at least a portion of the features and techniquesdescribed herein may be implemented. For example, architectures havingone or any number of processors 103 may be used, and such processors 103may be present in a single device or distributed among any number ofdevices. In one embodiment, single processor 103 handles communicationsas well as routing computations, while in other embodiments a separatededicated communications processor may be provided. In variousembodiments, different types of features or functionalities may beimplemented in a system according to the invention that includes aclient device (such as a tablet device or smartphone running clientsoftware) and server systems (such as a server system described in moredetail below).

Regardless of network device configuration, the system of the presentinvention may employ one or more memories or memory modules (such as,for example, remote memory block 120 and local memory 101) configured tostore data, program instructions for the general-purpose networkoperations, or other information relating to the functionality of theembodiments described herein (or any combinations of the above). Programinstructions may control execution of or comprise an operating systemand/or one or more applications, for example. Memory 120 or memories101, 120 may also be configured to store data structures, configurationdata, encryption data, historical system operations information, or anyother specific or generic non-program information described herein.

Because such information and program instructions may be employed toimplement one or more systems or methods described herein, at least somenetwork device embodiments may include nontransitory machine-readablestorage media, which, for example, may be configured or designed tostore program instructions, state information, and the like forperforming various operations described herein. Examples of suchnontransitory machine-readable storage media include, but are notlimited to, magnetic media such as hard disks, floppy disks, andmagnetic tape; optical media such as CD-ROM disks; magneto-optical mediasuch as optical disks, and hardware devices that are speciallyconfigured to store and perform program instructions, such as read-onlymemory devices (ROM), flash memory (as is common in mobile devices andintegrated systems), solid state drives (SSD) and “hybrid SSD” storagedrives that may combine physical components of solid state and hard diskdrives in a single hardware device (as are becoming increasingly commonin the art with regard to personal computers), memristor memory, randomaccess memory (RAM), and the like. It should be appreciated that suchstorage means may be integral and non-removable (such as RAM hardwaremodules that may be soldered onto a motherboard or otherwise integratedinto an electronic device), or they may be removable such as swappableflash memory modules (such as “thumb drives” or other removable mediadesigned for rapidly exchanging physical storage devices),“hot-swappable” hard disk drives or solid state drives, removableoptical storage discs, or other such removable media, and that suchintegral and removable storage media may be utilized interchangeably.Examples of program instructions include both object code, such as maybe produced by a compiler, machine code, such as may be produced by anassembler or a linker, byte code, such as may be generated by forexample a Java™ compiler and may be executed using a Java virtualmachine or equivalent, or files containing higher level code that may beexecuted by the computer using an interpreter (for example, scriptswritten in Python, Perl, Ruby, Groovy, or any other scripting language).

In some embodiments, systems according to the present invention may beimplemented on a standalone computing system. Referring now to FIG. 2,there is shown a block diagram depicting a typical exemplaryarchitecture of one or more embodiments or components thereof on astandalone computing system. Computing device 200 includes processors210 that may run software that carry out one or more functions orapplications of embodiments of the invention, such as for example aclient application 230. Processors 210 may carry out computinginstructions under control of an operating system 220 such as, forexample, a version of Microsoft's WINDOWS™ operating system, Apple's MacOS/X or iOS operating systems, some variety of the Linux operatingsystem, Google's ANDROID™ operating system, or the like. In many cases,one or more shared services 225 may be operable in system 200 and may beuseful for providing common services to client applications 230.Services 225 may for example be WINDOWS™ services, user-space commonservices in a Linux environment, or any other type of common servicearchitecture used with operating system 210. Input devices 270 may be ofany type suitable for receiving user input, including for example akeyboard, touchscreen, microphone (for example, for voice input), mouse,touchpad, trackball, or any combination thereof. Output devices 260 maybe of any type suitable for providing output to one or more users,whether remote or local to system 200, and may include for example oneor more screens for visual output, speakers, printers, or anycombination thereof. Memory 240 may be random-access memory having anystructure and architecture known in the art, for use by processors 210,for example to run software. Storage devices 250 may be any magnetic,optical, mechanical, memristor, or electrical storage device for storageof data in digital form (such as those described above, referring toFIG. 1). Examples of storage devices 250 include flash memory, magnetichard drive, CD-ROM, and/or the like.

In some embodiments, systems of the present invention may be implementedon a distributed computing network, such as one having any number ofclients and/or servers. Referring now to FIG. 3, there is shown a blockdiagram depicting an exemplary architecture 300 for implementing atleast a portion of a system according to an embodiment of the inventionon a distributed computing network. According to the embodiment, anynumber of clients 330 may be provided. Each client 330 may run softwarefor implementing client-side portions of the present invention; clientsmay comprise a system 200 such as that illustrated in FIG. 2. Inaddition, any number of servers 320 may be provided for handlingrequests received from one or more clients 330. Clients 330 and servers320 may communicate with one another via one or more electronic networks310, which may be in various embodiments any of the Internet, a widearea network, a mobile telephony network (such as CDMA or GSM cellularnetworks), a wireless network (such as WiFi, WiMAX, LTE, and so forth),or a local area network (or indeed any network topology known in theart; the invention does not prefer any one network topology over anyother). Networks 310 may be implemented using any known networkprotocols, including for example wired and/or wireless protocols.

In addition, in some embodiments, servers 320 may call external services370 when needed to obtain additional information, or to refer toadditional data concerning a particular call. Communications withexternal services 370 may take place, for example, via one or morenetworks 310. In various embodiments, external services 370 may compriseweb-enabled services or functionality related to or installed on thehardware device itself. For example, in an embodiment where clientapplications 230 are implemented on a smartphone or other electronicdevice, client applications 230 may obtain information stored in aserver system 320 in the cloud or on an external service 370 deployed onone or more of a particular enterprise's or user's premises.

In some embodiments of the invention, clients 330 or servers 320 (orboth) may make use of one or more specialized services or appliancesthat may be deployed locally or remotely across one or more networks310. For example, one or more databases 340 may be used or referred toby one or more embodiments of the invention. It should be understood byone having ordinary skill in the art that databases 340 may be arrangedin a wide variety of architectures and using a wide variety of dataaccess and manipulation means. For example, in various embodiments oneor more databases 340 may comprise a relational database system using astructured query language (SQL), while others may comprise analternative data storage technology such as those referred to in the artas “NoSQL” (for example, Hadoop Cassandra, Google BigTable, and soforth). In some embodiments, variant database architectures such ascolumn-oriented databases, in-memory databases, clustered databases,distributed databases, or even flat file data repositories may be usedaccording to the invention. It will be appreciated by one havingordinary skill in the art that any combination of known or futuredatabase technologies may be used as appropriate unless a specificdatabase technology or a specific arrangement of components is specifiedfor a particular embodiment herein. Moreover, it should be appreciatedthat the term “database” as used herein may refer to a physical databasemachine, a cluster of machines acting as a single database system, or alogical database within an overall database management system. Unless aspecific meaning is specified for a given use of the term “database”, itshould be construed to mean any of these senses of the word, all ofwhich are understood as a plain meaning of the term “database” by thosehaving ordinary skill in the art.

Similarly, most embodiments of the invention may make use of one or moresecurity systems 360 and configuration systems 350. Security andconfiguration management are common information technology (IT) and webfunctions, and some amount of each are generally associated with any ITor web systems. It should be understood by one having ordinary skill inthe art that any configuration or security subsystems known in the artnow or in the future may be used in conjunction with embodiments of theinvention without limitation unless a specific security 360 orconfiguration system 350 or approach is specifically required by thedescription of any specific embodiment.

FIG. 4 shows an exemplary overview of a computer system 400 as may beused in any of the various locations throughout the system. It isexemplary of any computer that may execute code to process data. Variousmodifications and changes may be made to computer system 400 withoutdeparting from the broader spirit and scope of the system and methoddisclosed herein. Central processing unit (CPU) 401 may be electroniccircuitry within a computing device that carries out the instructions ofprogrammable instructions by performing arithmetic, logic, controlling,and input/output (I/O) operations specified by the instructions. In someembodiments, CPU 401 may be an intelligent processing unit (IPU) tofacilitate processing of machine learning implementations moreefficiently by, in some embodiments, modeling data of knowledge asgraphs, with each vertex a measure of the probability of a particularfeature and the edges representing correlation or causation betweenfeatures. In this regard, each vertex links to only a few others so thegraph may be described as sparse. Massively parallel processing iscommonly used in applications with graphs, allowing work on multipleedges and vertices at the same time. Accordingly, calculations may becarried out in small words—often half-precision floating-point—so thatlow-precision data in a high-performance computing environment which maybe unlike traditional high-performance computing. Accordingly, the IPUis optimized for massively parallel, low-precision floating-pointcompute, and so it provides much higher compute density than othersolutions. In some embodiments, an IPU may hold a completemachine-learning model inside the processor and may have over 100 x morememory bandwidth than other solutions. This results in both lower powerconsumption and much higher performance. In some embodiments, CPU 401may be one or more graphics processing unit (GPU) comprising specializedelectronic circuit designed to rapidly manipulate and alter memory toaccelerate the creation of images in a frame buffer intended for outputto a display device 407. In some embodiments, GPUs may be used inembedded systems, mobile computing devices, specially designedcomputers, workstations, and gaming consoles, and other computingdevices. Such GPUs may be very efficient at manipulating computergraphics and image processing or be used for single instructionsmultiple data (SIMD) arrangement for faster processing. The highlyparallel structure of multiple GPUs may make them more efficient thanother CPUs for algorithms that process large blocks of data in parallel.In a personal computer, a GPU can be present on a video card or embeddedon the motherboard. In certain CPUs, they may be embedded on the CPUintegrated circuit. CPU 401 is connected to bus 402, to which bus isalso connected memory 403, nonvolatile memory 404, display 407, I/O unit408, and network interface card (NIC) 413. I/O unit 408 may, typically,be connected to keyboard 409, pointing device 410 (or in someembodiments, a human interface device), hard disk 412, and real-timeclock 411. NIC 413 connects to network 414, which may be the Internet ora local network, which local network may or may not have connections tothe Internet. Also shown as part of system 400 is power supply unit 405connected, in this example, to ac supply 406. Not shown are batteriesthat could be present, and many other devices and modifications that arewell known but are not applicable to the specific novel functions of thecurrent system and method disclosed herein. It should be appreciatedthat some or all components illustrated may be combined, such as invarious integrated applications (for example, Qualcomm or SamsungSOC-based devices), or whenever it may be appropriate to combinemultiple capabilities or functions into a single hardware device (forinstance, in mobile devices such as smartphones, video game consoles,in-vehicle computer systems such as navigation or multimedia systems inautomobiles, or other integrated hardware devices).

In various embodiments, functionality for implementing systems ormethods of the present invention may be distributed among any number ofclient and/or server components. For example, various software modulesmay be implemented for performing various functions in connection withthe present invention, and such modules may be variously implemented torun on server and/or client components.

Conceptual Architecture

FIG. 5A is a block diagram illustrating an exemplary systemarchitectural arrangement of system components, according to a preferredembodiment of the invention. According to the embodiment, a digitalmedia arrangement system 550 based on an attention condition comprises amedia compiler computer 423 a memory 403, an image capture componentand/or one or more sensing components 512, a display component 407, oneor more processors 210, and a plurality of programming instructions.Image capture component 512 is operable to receive images and convertthem to digital media; however, in some embodiments, systems and methodsdescribed herein may operate over a live image feed without any image orimages stored or recorded. It should be appreciated by one with ordinaryskill in the art that capturing digital images, also known asdigitization is the process of creating a digital image file directlyusing a camera, or scanner. Components 512 may comprise, but not limitedto, infrared (IF) image capture, 3D image capture, and further compriseadditional sensing elements like WiFi, Bluetooth, network signal sensorsthat, for example, can be used for tracking of individuals. Thedigitization process may require both hardware and software and may be acreation of a digitally encoded representation of the visualcharacteristics of an object such as a physical scene, the interiorstructure of an object, a person or group of people, or some otherobject. The term is often assumed to imply or include the processing,compression, storage, printing, and display of such images. A keyadvantage of a digital image, versus an analog image such as a filmphotograph, is the ability to process understand characteristics of theimage to, at least, improve the computer to perform business functions.Algorithms described herein disclose systems and methods to gainhigh-level understanding from the captured digital images or videos toprogrammatically acquire, process, analyze and understand captureddigital images, and extraction of high-dimensional data from particularenvironments in order to produce numerical or symbolic information.Understanding in this context means the transformation of visual images,by media compiler computer 423, into descriptions of businessenvironments that can interface with thought processes of subjectswithin the images to elicit intelligent appropriate action. This imageunderstanding can be seen as the disentangling of symbolic informationfrom image data using models constructed with the aid of geometry,physics, statistics, and learning theory.

Further according to the embodiment, metadata manager 523 may receive aplurality of metadata describing a digital media segment comprising aplurality of captured images (for example, digital video) captured byimage capture component 512, the metadata comprising, at least, apriority marker for each frame (or image) of a plurality of frames (orvideo) and further comprise one or more set durations, each set durationbeing an amount of time associated to a set of frames, the set of framescomprising one or more frames, or at least a portion of the plurality offrames. In an embodiment where a plurality of images comprising humansubjects is captured, facial analytics may be used to identify, usingone or more images, a user profile associated to the user, the userprofile comprising, at least, one or more attention conditions. Itshould be noted that, in some embodiments, data collected in systems andmethods described herein may be captured in an anonymous fashion toadhere to local data privacy laws. For example, any personal identifyinginformation may not be included, may be obfuscated or encrypted usingencryption methods known in the art. A facial analytics system, byanalyzer 502, comprises computer vision technology capable ofidentifying or verifying a person from one or more digital image, aplurality of images, a video frame from a video source, and the like.Analyzer 502 may utilize multiple methods of facial recognition systemswork, but in general, a comparison of selected facial features fromgiven image with faces within user database 524, or within a userprofile from user database 524. In some embodiments, a biometricartificial intelligence, as is known in the art, may be used to uniquelyidentify a person by analyzing patterns based on a person's facialtextures and/or shape.

In some embodiments, media compiler computer 423 may be a cloud-basedservice receiving images from and delivering media to a mobile userdevice 506; however, in other embodiments media compiler computer 423may reside at least partially on a specially programmed computer (forexample, a self-contained advertisement display unit) with at least anattached image capture component 512 and display 407 or reside at leastpartially within a mobile device 506. An advantage to systems disclosedherein is its contactless and non-invasive process to capture images,recognize one or more users, and process/compile sets of frames. Inother embodiments, systems may include advanced human-computerinteraction (for example, behavioral analysis, product interactions,object (non-screen) related interactions, and the like), videosurveillance, automatic indexing of images and/or frames, and videodatabase, among others.

In a preferred embodiment, one or more attention conditions comprisingat least an attention span may be computed, real-time by attentiondetector 522, to determine details of an attention level for a useridentified in one or more of the captured images for one or moresubjects within the images (see FIGS. 6A and 9). Media compiler computer423 further comprises content compiler 525 for compiling a new digitalmedia sequence or segment by rearranging at least a portion of theplurality of frames or subsegments or clips of a pre-configured file orfrom stored, streaming video, or a combination of both, therearrangement optionally including, but not limited to, additionalelements such as generated text, zooming in on focal items within themedia (for example, products, people, graphics, images, one or moreframes, etc. as described in FIGS. 10A and 10B), and other techniquesfor emphasizing key elements of the content. It should be noted that, insome embodiments, the additional elements may be preconfigured ordirected or determined via metadata or in some other fashion. In apreferred embodiment, the compiling, recompiling, and/or rearrangementmay be based, at least, on priority markers associated to one or moreframes as well as an importance and/or attention rating for additionalelements (as described in FIGS. 10A and 10B) in conjunction with detailsfrom the attention condition. In some embodiments, compiler 525 maycompile simultaneously as the attention condition is detected. Mediacompiler computer 423 further comprises a content deliverer 501 fordelivering content, for example, a compiled, or at least a portion of anoriginal digital media to display 407. In some embodiments, contentdeliverer may deliver the digital media to user device 506. Mediacompiler computer 423 may further comprise interface 505 for displayinga dashboard of information to, for example, an administrator device (viaa user device 506). Such a dashboard may be operable to receive digitalmedia for use by media compiler computer 423, allow keyframe 911 markersto be assigned to digital media (see FIG. 9B).

Media compiler computer 423 may further comprise external datasets 520for incorporating additional information such as location information(e.g. pre-configured and associated to media compiler computer 423, GPScoordinates from a device, geolocation, location information received byanother network connected device), demographic information (for example,computed through facial recognition, received from a user profile fromuser database 524, or received from another network-connected device),mood information from facial recognition, financial information, socialinformation such as accessing an identified user's information throughcommon social media platforms such as Facebook™ or LinkedIn™. In someembodiments, external datasets 521 is used in lieu of or in conjunctionwith external datasets 520 to receive external information. In someembodiments, media compiler computer 423 may include additional elementsas described in FIGS. 10A and 10B.

Media compiler computer 423 may further comprise media database 504 forstoring media files uploaded by an administrator and media received fromimage capture component 512. Compiled media may be stored in mediadatabase 504 by content compiler 525. Media compiler computer 423 mayfurther comprise repository 540 for storing datasets collected fromexternal datasets 520, external datasets 521, or other sources. Mediacompiler computer 423 may further comprise user database 524 that maystore user profile information for known user. Profiles may beassociated to a user of user device 506 or be correlated with previousrecognized users previously recognized by analyzer 502. User profilesmay comprise attention information and be averaged over a number ofrecognized or computed attention conditions. It can be appreciated that,in some embodiments, compiler 525 may use an average attention span inrequencing clips instead of methods described previously.

Media compiler computer 423 may further comprise content manager (CMS)501 to collect metadata, manage external datasets and combine thecollected information for algorithms disclosed herein.

FIG. 5B is a block diagram illustrating an exemplary architecturalarrangement of system components, according to a preferred embodiment ofthe invention. According to the embodiment, a content managementsolution 501 may be used to present media content to users and may beenhanced through the use of facial analytics software 502 operating on acontent delivery device 510 that may be any suitable computing device,for example, a mobile computing device or computer. Facial recognitionmay be used to calculate a person's information 503, for example,including but not limited to, age, gender, mood state, and attentionspan. This information may be collected via a camera 512 using computervision (as described previously), which may for example be automaticallytriggered when a person is detected 511 (for example, using any of avariety of facial recognition techniques and, for example, an always-oncamera). Collected data may then be timestamped, geolocated (if locationinformation is available or may be derived from other availableinformation (for example, from external datasets 520 and/or 521), andstored in a database 504, for example on a network-connected server(this provides a decentralized system where personal data may be storedseparately from the facial recognition 502 or content management 501components). Additional information may be incorporated from a varietyof external datasets 520, including but not limited to, externalgeolocation or demographic information, and available information may bepresented via a dashboard interface 505 to user device 506 such as asystem administrator. Programming instructions when executed by theprocessor may cause the processor to use a content management solution(CMS) 501 to input and deliver content components (including, but notlimited to, audio, video, images, virtual reality (VR), augmentedreality (AR), games, social media, text, and the like), online serversto store the data, and data dashboards 505 to display the interpreteddata.

FIG. 5C is a block diagram illustrating an interaction of a plurality ofalgorithms as they relate to the data capture and the content delivery,according to a preferred embodiment of the invention. According to theembodiment, when performing facial recognition and analytics,programming instructions 502 executed on a processor cause the processorto use facial landmarks and a head position to plot key featurelocations and relate them to a method that predicts accuracy levels foreach parameter (for example, selected facial features from a givenimage). Additional captured data points include screen interactions,gestures, movement, and interactions with, for example other people orobjects (for example, a subject interacts with an object within theframe, such as a first subject picks up a can of Pepsi™ and a secondsubject picks up a can of Coca Cola™). Further to the captured data, asystem and method may integrate additional external datasets 520 toenhance interpretation of a circumstance under which a user interactswith the content. These data points may include, but are not limited to,weather, public holidays, local events, traffic, or other information.All of the datasets, live (as retrieved from, and stored in, a real-timedatabase 535) and historic (as may be retrieved from a repository 540 ofhistorical data), may then be combined with a calculated attention spanof the user (as digitally captured by the camera 512) to ensure that thekey content messaging is delivered to that user. Users are interpretedby the facial analytics software and their calculated attention span maybe sent to a server as metadata. A machine learned data interpretationalgorithm 536 may detect and assesses patterns in the attention spanfluctuations through a minute of the hour, hour of the day, day of theweek, week of the year, and the like, to predict viewing habits ofpeople on an associated piece of content, screen, and location. Furtherdatasets 520 may be integrated in a predictive algorithm to a customer'sdemographic and viewing mood to compute how long to play a piece ofcontent to actively engage viewers with the right content and the righttime. It should be noted that the content may include one or moredigital media restructuring formats that includes, but is not limitedto, generated text, zooming in on focal items (for example, products,people etc.), and other techniques for highlighting key elements withinthe content. Additional external datasets 520 may also be added to thepredictive algorithm 536 to improve accuracy. The predictive algorithm536 may then utilize all collected data and analyses performed to selectcontent files 530 for presentation, which may then be further refinedthrough a content optimization algorithm 531 to present the optimumcontent based on the user data, attention span, and other analysis.Content may then be presented through the CMS 501, and operationcontinues.

Detailed Description of Exemplary Embodiments

FIG. 6A is a flow diagram illustrating a calculation of an attentioncondition, according to a preferred embodiment of the invention.According to the embodiment, a camera 512 operating on a media compilercomputer 423 may be enabled and scanning for a human face 603 (alsoreferred to herein as a subject). If the camera is disabled, a contentdelivery application may be restarted 602 to ensure the software isworking properly and facial analytics are enabled. If no face isdetected, a timer may be calculated based on the current frame of amedia content file being presented 608, and this information is returned609 to a real-time database 535 as operation continues. This operationcauses timing information to be collected, identifying how long mediacontent was presented before a user looked away (lack of a human faceaccording to the camera 601), indicating a loss of attention. Thisinformation is collected and used in determining a subject's attentionspan, adding to data from when a subject's face was detected, which inturn indicates the beginning of user attention 604. In addition to thepresence or lack of a face, gaze detection (see FIG. 6B) may be utilized605 to identify where a subject may be looking, refining analysis ofattention span by identifying not only when a subject is present, butalso when they are actively viewing the content being presented. If thesubject's face is “in angle” (that is, the face is determined to be atan angle indicating an attention condition or viewing), the attentionspan timer continues 606 and operation loops as shown. If the subject'sface is determined to be not-in-angle, as when a user looks away, thetimer may be paused 607 and then the subject's attention span may becalculated using the timer and frame information 608 and incorporatedinto the live database 609.

In some embodiments, gaze detection (and attention detection, ingeneral) may utilize eye tracking by measuring either the point of gaze(where a subject is looking) or motion of an eye relative to a headassociated to a subject. In this regard, an eye tracker process ordevice may be used to measure eye positions and eye movement as an inputdevice for human-computer interaction. Though there are a number ofmethods for measuring eye movement that may be used, in such anembodiment, video images of a subject where an eye position is extractedmay be preferable. Other methods may include search coils or based onthe electrooculogram. In other embodiments, one or more overlay layersto control and adjust output at a pixel level (as is known in the art)may be used to incorporate and/or combine pixel masks, lenses, and/or aplurality of layers configurable as lenses to output specific portionsof the display to a user's left and right eyes. For example, using gazetracking technology, portions of a display and one or more matrix layerscan be activated or deactivated to present different images to a user'sleft and right eye, and thus perform eye-tracking thereon. Otherembodiments may use microlenses, circular lenses, panels with controlledliquid crystal density and the like.

FIG. 6B is an illustration defining a head gaze angle, according to apreferred embodiment of the invention. According to the embodiment aplurality of digital images generated from a camera indicating asubject's head position are shown. Accordingly, a subject's attentionmay be inferred from the angle of their head and direction of theirgaze, identifying when a subject's face may be within a camera frame,but their attention is directed at something other than the content filebeing presented. In some embodiments other types of tracking technologymay be used such as eye tracking, and the like. A subject's face may beconsidered “in angle” 610 when their gaze is directed at the contentbeing presented, even when the angle of their head may not be ideal (asshown). For example, the subject may be viewing content on a smartphoneand holding the phone at an angle, or they may tilt their head duringviewing (such as to read text in the media content that may be displayedat an angle). When a subject's gaze is directed in another direction,again regardless of the actual angle of the head, it may be identifiedas “not in angle” 620 and taken as an indication that the user'sattention is focused elsewhere, that is, not focused on presentedcontent. For example, the subject may become distracted and look away,or they may move their phone as they direct their attention elsewhere,causing the camera to identify that the subject's face is not in angle.

FIG. 7 is a block diagram illustrating a structure of a contentmanagement solution (CMS) logic 700, according to a preferred embodimentof the invention. According to the embodiment, in a first step 701, CMS501 may be running on a content delivery device (for example a mobilecomputing user device 506). During operation, CMS 501 may check forInternet connectivity in step 702, and if the network connection isunavailable a notification may be presented to the user in step 721 (viathe user device 506) prompting them to restart the software in step 723(or, optionally, automatically restarting the software or components ofthe software such as a networking component that may be restartedindividually). If connectivity is available, the CMS 501 may check forlocation services in step 703, and again if unavailable a user may benotified in step 720 and the software or necessary software componentmay be restarted in step 723. If location services are available, forexample, GPS or network-based location services, a device ID may beprovided to the CMS 501 in step 704 for use in identifying theparticular device being used. In some embodiments, the location may bepre-configured (for example, when the system is stationary, such as inan electronic billboard configuration where content may be displayed toa plurality of subjects). The device ID may be a hardware ID such as anIMEI or MAC address, or it may be a fingerprint-type ID used to identifythe particular device from available hardware or software detailsincluding, but not limited to, brand, model, screen size, hardwaresensors, keyboard layout, battery capacity, or operating system version.CMS 501 then checks to see if there is new content selected for thedevice ID in step 705, and if so any running timers may be paused insstep 710 to allow the new content to play while attention span analysisruns. If no new content is available, CMS 501 may check forpreviously-stored content in step 706, and if available plays a storedcontent file in step 711. If no stored file exists, the device ID may beshown in step 712 so that content may be manually associated with thedevice ID (for example, so that an administrator may manually initiate acontent store for the device, before letting content selection logic totake over and curate content going forward).

FIG. 8 is flow diagram outlining trigger functionality, according to apreferred embodiment of the invention. As shown, multiple nested triggerevents may be used to select and present content to a user, with areverse hierarchy from most general to most specific (such that themost-specific available trigger is used, and more generalized triggerconditions are used when the more specific conditions are not met).According to the embodiment, an initial trigger condition may check forthe presence of a human user in step 801 (for example, by analyzing oneor more digital images), playing an appropriate video in step 810 when aperson is detected. If more specific information is available, agender-based trigger condition may be checked in step 802, as well as anage-based trigger in step 803. If either of these triggers is met, acombined gender/age-based video may be presented in step 820, but ifboth conditions fail a fallback video loop may be shown instead in step830. In addition to the video loop, further trigger conditions may bechecked such as a mood trigger in step 804, which may play mood-specificcontent in step 840 when met (such as playing specific videos when auser is detected to be happy or excited, for example) or machine-learnedtriggers in step 805 which may vary according to machine learning fromhistorical data, and may be based on arbitrarily-complex data points orcombinations. Such complex additional triggers may be developed using amachine learning model in step 851 that is processed by CMS 501 in step850 to train on data over time, and when any such triggers are met atrigger-specific video may be selected and played in step 806, beforefalling back to the video loop in step 830.

FIG. 9A is a flow diagram illustrating a smart content deliverymechanism, based on an attention condition, according to a preferredembodiment of the invention. As shown, operation of a content deliverymechanism may run in a cyclical fashion, iterating over content and datainputs continuously in a self-learning operation that adjusts asconditions change. In a first step 901, a subject (for example, aperson) may be detected, by analyzer 502 from a plurality of imagescaptured by image capture component 512. In some embodiment a profilecorresponding to a user may be retrieved from user database 524. In anext step, 902, attention detector 522 may detect and measure theperson's attention span (referring to FIG. 6A). In a next step 903, theattention is measure as a factor of time by attention detector 522. Dataassociated to attention span including at least the duration (i.e. themeasured time factor) comprise the attention condition. In someembodiments an attention condition must meet predefined thresholds todetermine whether a subject is in an attention condition. In someembodiments the attention condition may further comprise additional dataassociated to the attention condition, for example, head positioninginformation (as described in FIG. 6B), demographic information, a subsetof durations of each position, and the like. In a next step 904,information from the attention condition, may be incorporated into acontent selection sequencer (as described in FIG. 9B) to select digitalmedia content segments for presentation in order to optimally compile,in step 905, one or more frames based on a computed attention spanlength, by content compiler 525. In some embodiments, step 904 mayinclude additional elements as described in FIGS. 10A and 10B. In a nextstep 906, when an optimum sequence of content segments is determined,the resultant content may be presented to display 407 (or in someembodiments, to user device 506) for viewing. In a preferred embodiment,operation returns to step 901 to determine whether or not the subject isstill in an attention condition. It should be appreciated that, in someembodiments, a plurality of attention conditions, or elements within oneor more attention conditions may operate together, or separately, toaffect the content algorithm. For example, a detected mood and/or gendermay affect one or more attention conditions.

FIG. 9B is an illustration showing the optimization of video content,according to a preferred embodiment of the invention. According to theembodiment, a content optimization algorithm may split content segmentsusing keyframes 911, dividing a plurality of frames, a digital mediasegment, a video, or the like into a subset of frames of varying length912, 913, 914 that comprise logical sub-portions of the content (thatis, a subset of frames, also referred to herein as a clip). In someembodiments, preconfigured metadata is pre-configured by metadatamanager 523 describing one or more frames of the content, including, butnot limited to, a duration of each segment, a priority, or the like.Accordingly, the subset of frames may be ranked by priority according totheir relative importance, for example an informational clip withcontent that is deemed most important by the content creator, forexample, key product features or information, company information,company contact information, a sales hook, a call to action, and thelike, may be determined to be more important than, for example, a clipof a user interacting with a product, a coverage shot, and the like. Inthis regard, an ordered ranking of subset of frames within a singlecontent file, may be compiled, by content compiler 525, into one or morecombinations according to different attention conditions. It should beappreciated that importance of content segments and ranking may belearned and/or driven by data generated from the image/sensor analytics.In some embodiments a plurality of subset framed may be compiled usingboth current interaction data or historic data, or a combination ofboth, to inform content in advance of a target attention condition beingcomputed or including additional elements ranked by importance andattention (referring to FIGS. 10A and 10B). In other embodiments,content compiler compiles a new media segment dynamically in real-time.When selecting content for presentation, the subset of frames may beretrieved and compiled based on an associated length of time and thecalculated attention condition associated to a particular subject, suchthat the total length of all selected frames is less than or equal tothe attention span. In some embodiments, additional elements may beincluded in the compiled media such as, but not limited to, text, zoomand other methods of highlighting the key message (as described in FIGS.10A and 10B). Accordingly, if, for example, a user's attention span, forexample, as determined by head gaze and/or eye tracking, is determinedto be no more than one second long, the most-important one-second clip912 may be selected and presented by itself, or first in a sequence offrames (e.g. in order, 912, 913, 914). If, however, the user's attentionspan is determined to be longer than, for example, one second but lessthan four seconds, the same one-second clip of highest importance 912may be selected, as well as a three-second clip of next-highestimportance 913, so that a total length of the compiled (i.e.stitched-together) content substantially matches the attention span offour seconds. If the user's attention span is longer still, the entireoriginal media content of eight seconds may be compiled to reassemblethe original video without rearrangement or deletion of frames, or insome embodiments a different sequence. As shown, the subset of framesmay be played back in their original order so as to preserve the contentand logical flow of the media, while ensuring that only the mostimportant clips are shown based on the user's attention span.

In an exemplary embodiment, a first clip 920 may present a rankedwhereby segment 1 912 (that is, a subset of frames comprising 912) isthe most important segment, and segment 3 914 is the least important. Inthis regard, if the attention condition had an associated attention spanof one second (or less), compiler 525 may isolate only the first segment912 (that, is delete segments 2 913 and segment 3 914) and deliver onlysegment 1 912 as output media 930 to display 407.

In another exemplary embodiment, if the attention condition had anassociated attention span of 4 seconds (or less), compiler 525 maycompile segments 1 912 and 2 913 (thereby compiling a media output 940of 4 seconds in total length) and deliver media 940 output to display407.

In yet another exemplary embodiment, if the attention condition had anassociated attention span of 8 seconds (or less), compiler 525 maycompile segment 1 912, segment 2 913, and segment 3 914 (therebycompiling a media output 950 of 8 seconds in total length) and delivermedia 950 output to display 407.

FIG. 10A is an illustration showing an exemplary arrangement ofadditional elements according to a preferred embodiment of theinvention. According to the embodiment, arrangement 1001 may representone or more visual elements such as text overlay, overlay of stillimages, focused video segments, graphic overlay such as a logo or othergraphical item, and the like. These visual elements may then be placedor overlaid in a priority sequence based on attention and importanceranking in a video segment for visual emphasis as priority elements,within media, such that viewers may see information in a prioritysequence based on parameters set within the video (referring to FIG.10B). Accordingly, arrangement 1001 may comprise on or more text overlaysections 1002 comprising a plurality of letters, words or phrases (forexample, words associated to a product offering); one or more imageoverlay sections 1003 comprising a focus to a segment of video or anoverlaid still image, or a combination of both (for example, an image orframe of a user of the product offering); one or more image overlaysection 1004 comprising an overlaid still image (for example, theproduct offering); one or more graphical sections 1005 comprising agraphical item such as a logo or a log combined with letters (forexample, a logo for a company providing the product offering). It shouldbe appreciated by one with ordinary skill in the art that elements 1002,1003, 1004 and 1005 illustrate an exemplary embodiment and that anynumber of these elements may be arranged within arrangement 1001.

In an exemplary embodiment, arrangement 1001 may rank items with anattention ranking from highest to lowest rank as 1003, 1005, 1002, and1004. Further, arrangement 1001 may rank items with an importanceranking from highest to lowest as 1004, 1005,

FIG. 10B is an illustration showing an exemplary arrangement 1010 ofoptimized additional elements according to a preferred embodiment of theinvention. According to the embodiment, additional elements may berearranged or optimized based on an attention ranking and/or animportance ranking. For example, elements 1002, 1003, 1004 and 1005 mayhave a designated ordered attention ranking of 1004, 1005, 1002, and1003, and an important ranking of the same (i.e. 1004, 1005, 1002, and1003). Accordingly, rearrangement 1010 illustrates an optimizedarrangement of the additional elements emphasizing the most importantelements. For example, arrangement 1010 emphasizes, in order ofimportance, product image (or one or more frames) of product 1004 andoverlaid graphic 1005 (i.e. the company logo), text section 1002, andimage (or sequence of frames) 1003 (for example, the user). Accordingly,an emphasis on, for example, the product and company are optimized to,for example, increase sales or present important elements in anoptimized ranked fashion.

The skilled person will be aware of a range of possible modifications ofthe various embodiments described above. Accordingly, the presentinvention is defined by the claims and their equivalents.

What is claimed is:
 1. A digital media arrangement system based on anattention condition comprising: a media compiler computer comprising amemory, an image capture component, a display component, one or moreprocessors, and a plurality of programming instructions, the pluralityof programming instructions stored in the memory and when executed bythe one or more processor, cause the one or more processor to: receive adigital media segment from an administrator device; receive a pluralityof metadata describing the digital media segment, the metadatacomprising, at least, a priority marker for each frame of a plurality offrames and one or more set durations, each set duration being an amountof time associated to a set of frames; capture, by the image capturecomponent, an image of a user; identify, using the image, a user profileassociated to the user, the user profile comprising, at least, one ormore attention conditions, and demographic information associate withthe user; update the priority marker for each frame of at least aportion of the plurality of frames based on the user profile;dynamically compile a new digital media segment by resequencing, atleast, a portion of the plurality of frames and additional elements, therearrangement based on, at least, the updated priority markersassociated to each frame; display, at a display component, the newdigital media segment; wherein at least a portion of the plurality offrames, of the new digital media segment, are focused segments, thefocused segments based on an importance ranking; wherein the focusedsegments are comprised of a restructured format by zooming one or morefocal items.
 2. The system of claim 1, wherein an attention condition isa duration of time based on a timed calculation of a user's headposition being in an in-angle arrangement.
 3. The system of claim 1,wherein an attention condition is an average duration of time based on acalculation of a plurality of durations of a user's head position in anin-angle arrangement.
 4. The system of claim 1, wherein the attentioncondition is a duration of time, the duration of time associated to theuser's attention based on a plurality of previously stored durations oftime based on context.
 5. The system of claim 1, wherein the new digitalmedia segment comprises a sequence of frames wherein the total durationof time of the sequence of frames is equal to or less than a durationassociated to the first attention condition.
 6. The system of claim 1,wherein if a duration associated to the first attention condition isequal to or greater than the digital media segment, no rearrangement isdone.
 7. The system of claim 5, wherein a selection of a secondattention condition is selected instead of the first attentioncondition, the selection based on external data.
 8. The system of claim7, wherein the additional elements are selected from a group consistingof a time-of-day, a location, demographic information associated to theuser, mood, and behavior.
 9. The system of claim 1, wherein the digitalmedia segment and the plurality of metadata come from a database insteadof an administrator device.
 10. A computer-implemented method forcomputing a digital media arrangement, the method comprising the stepsof: receiving a digital media segment from a network connectedadministrator device; receiving, from the administrator device via anetwork, a plurality of metadata describing the digital media segment,the metadata comprising, at least, a priority marker for each frame of aplurality of frames and a duration of a set of frames associated to asame priority marker; capturing, by the image capture component, animage of a user; identifying, using the image, a user profile associatedto the user, the user profile comprising, at least, one or moreattention conditions, and demographic information associate with theuser; update the priority marker for each frame of at least a portion ofthe plurality of frames based on the user profile; dynamically compilinga new digital media segment by resequencing, at least, a portion of theplurality of frames and additional elements, the rearrangement based on,at least, the updated priority markers associated to each frame;displaying, at a display component, the new digital media segment;wherein at least a portion of the plurality of frames, of the newdigital media segment, are focused segments, the focused segments basedon an importance ranking; wherein the focused segments are comprised ofa restructured format by zooming one or more focal items.
 11. The methodof claim 10, wherein an attention condition is a duration of time basedon a calculation of a user's head in an in-angle arrangement.
 12. Themethod of claim 10, wherein an attention condition is an averageduration of time based on a calculation of a plurality of durations of auser's head in an in-angle arrangement.
 13. The method of claim 10,wherein the attention condition is a duration of time, the timeassociated to the user's attention based on historical data in context.14. The method of claim 10, wherein the new digital media segmentcomprises a sequence of frames wherein the total duration of time of thesequence of frames is equal to or less than a duration associated to thefirst attention condition.
 15. The method of claim 10, wherein if aduration associated to the first attention condition is equal to orgreater than the digital media segment, no rearrangement is done. 16.The method of claim 14, wherein a selection of a second attentioncondition is selected instead of the first attention condition, theselection based on external data.
 17. The method of claim 16, whereinthe additional elements are selected from a group consisting of atime-of-day, a location, demographic information associated to the user,mood, and behavior.
 18. The method of claim 10, wherein the digitalmedia segment and the plurality of metadata come from a database insteadof an administrator device.